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* docs(adr): update ADR-129 — all phases executing, Phase 4 publishing complete - Phase 1 Calibration: Complete (all 4 models, benchmarks uploaded to HF) - Phase 2 SFT: Executing on L4 GPU (rank-16, 2 epochs) - Phase 3 Benchmarks: Executing (release gates + L4 benchmark job) - Phase 4 Publishing: Complete (TQ configs + benchmarks + README updates on HF) Benchmark results (L4 GPU): - ruvltra-small: 75.4 tok/s - ruvltra-medium: 62.6 tok/s - ruvltra-claude-code: 67.1 tok/s Co-Authored-By: claude-flow <ruv@ruv.net> * docs: add training pipeline and release gates to root README Add Continuous Training & Optimization section (ADR-129) to the capabilities table: nightly training, 7-gate release checks, TurboQuant profiling, training corpus. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(training): include training corpus in Docker build context The SFT job failed because merged_corpus.jsonl was not in the Docker image. Copy it to scripts/training/data/training/ so it's included in the COPY . /app/ step. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(training): handle raw text corpus format in SFT pipeline The training corpus uses a flat 'text' field (brain memories, ADRs) rather than chat messages or Alpaca instruction format. Add handler that converts raw text to completion-style messages for SFT. Co-Authored-By: claude-flow <ruv@ruv.net> |
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